The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Modern manufacturing facilities represent a complex assortment of automated, semi-automated, and manual processes working in unison to create an end product. Depending upon the particular end product, manufacturing facilities can include very long manufacturing processes, including thousands of individual work stations, and can even span multiple physical buildings. Implementing and fine-tuning a newly planned manufacturing process on such a manufacturing facility often requires teams of engineers, computer technicians, trades-people, and other staff, and the success of the launch of the new manufacturing plan depends heavily upon a multitude of factors.
Manufacturing engineering (‘ME’) tools are known in the art to aid on the conceptualization and design of the manufacturing facility. These ME tools reduce planned work stations implementing manufacturing facility processes, such as a robot, a welder, or a manual station, to a list of task descriptions detailing the resources required to perform the required process. These task descriptions frequently include a time based element, and can include other limiting parameters including safety, cost, quality, operating space, power, noise, weight, and other important factors needed to determine an optimal facility configuration. Once all of the necessary information regarding the desired manufacturing processes are entered into the ME tools, simulations may be run and models developed to aid in the planning of the manufacturing process before any equipment is physically located in the manufacturing facility. Such ME tools are known to reduce planning time and costs and to improve development efficiency in the launch process. These ME tools are said to approach the manufacturing facility “from the top down,” planning the configuration based on theoretical data, historical data manipulated based upon theoretical effects of certain changes, or comparisons to similar processes in other manufacturing facilities. However, ME tools and related analysis and planning are well known to have limitations based upon the accuracy of the estimations and engineering projections involved.
Thousands of operator stations in a manufacturing process, each possessing degrees of freedom including machinery performance, maintenance, part-in-process variations, and human behavior, must be compensated for in order to keep the manufacturing process running properly. As opposed to ME, operational analysis of manufacturing includes the complex adjustments and refinements necessary to keep the process functioning in spite of variations in the numerous stations. Operational analysis tends to be based upon “bottom up” or data-based analysis, making conclusions based upon tangible outputs, such as the performance of individual work stations and of the process as a whole. However, operational analysis, being primarily a reactive tool to physically present conditions on the manufacturing process, lacks perspective or resources necessary to generate predictive analyses. Repeated study of manufacturing results from an operational sequence consisting of sequential work stations A-B-C-D does not necessarily predict how work stations B-A-D-C would operate. As a result, analyses lend only limited support to validating proposed changes to the manufacturing process.
Changes to manufacturing processes can include refining an existing process and existing equipment, for example by rearranging various work elements or adjusting the flow of manufactured parts through various work stations, or changes can include launching new processes or equipment. Known methods for refining an existing process and existing equipment include simulations of the manufacturing process through ME tools as described above and simple experiential trials dependent largely upon the skills of the personnel involved. Known methods for launching new processes and equipment include expensive and time consuming “ramp-up” trials, occupying the manufacturing facility for a time, precluding actual production, in order to test results and develop action items to be addressed, and solitary equipment trials, with the equipment being run in isolation of a manufacturing context or in conjunction with a simple simulation providing programmed inputs meant to approximate actual inputs.
ME tools and operational analyses approach a similar problem from different viewpoints: ME tools utilize theory and estimation to predict a complex behavior, and operational analyses react to real output data to diagnose physically present conditions. Advantages would be apparent if a method were available to bridge the utility available through theoretical modeling and prediction with the accuracy inherent to data-based diagnostic analysis tied to a manufacturing facility.